prepScores: Prepare scores for region based (meta) analysis
In seqMeta: Meta-Analysis of Region-Based Tests of Rare DNA Variants

Description Usage Arguments Details Value Note Author(s) References See Also Examples

This function computes and organizes the neccesary output to efficiently meta-analyze SKAT and other tests. Note that the tests are *not* computed by these functions. The output must be passed to one of skatMeta, burdenMeta, or singlesnpMeta.

Unlike the SKAT package which operates on one gene at a time, these functions are intended to operate on many genes, e.g. a whole exome, to facilitate meta analysis of whole genomes or exomes.

prepCox(Z, formula, SNPInfo = NULL, snpNames = "Name",
  aggregateBy = "gene", data = parent.frame(), verbose = FALSE)

prepScores(Z, formula, family = stats::gaussian(), SNPInfo = NULL,
  snpNames = "Name", aggregateBy = "gene", kins = NULL, sparse = TRUE,
  data = parent.frame(), verbose = FALSE)

prepScoresX(Z, formula, male, family = stats::gaussian(), SNPInfo = NULL,
  snpNames = "Name", aggregateBy = "gene", kins = NULL, sparse = TRUE,
  data = parent.frame(), verbose = FALSE)

`Z`	A genotype matrix (dosage matrix) - rows correspond to individuals and columns correspond to SNPs. Use 'NA' for missing values. The column names of this matrix should correspond to SNP names in the SNP information file.
`formula`	Base formula, of the kind used in glm() - typically of the form y~covariate1 + covariate2. For Cox models, the formula follows that of the coxph() function.
`SNPInfo`	SNP Info file - must contain fields given in 'snpName' and 'aggregateBy'.
`snpNames`	The field of SNPInfo where the SNP identifiers are found. Default is 'Name'. See Details.
`aggregateBy`	The field of SNPInfo on which the skat results were aggregated. Default is 'gene'. For single snps which are intended only for single variant analyses, it is recomended that they have a unique identifier in this field.
`data`	data frame in which to find variables in the formula
`verbose`	logical. whether or not to print the progress bar.
`family`	either gaussian(), for continuous data, or binomial() for 0/1 outcomes. Binary outcomes are not currently supported for family data.
`kins`	the kinship matrix for related individuals. Only supported for family=gaussian(). See lmekin in the kinship2 package for more details.
`sparse`	whether or not to use a sparse Matrix approximation for dense kinship matrices (defaults to TRUE).
`male`	For analyzing the X chromosome, with prepScoresX, ‘male’ is the gender (0/1 or F/T) indicating female/male. See details.

This function computes the neccesary information to meta analyze SKAT analyses: the individual SNP scores, their MAF, and a covariance matrix for each unit of aggregation. Note that the SKAT test is *not* calculated by this function. The output must be passed to one of skatMeta, burdenMeta, or singlesnpMeta.

A crucial component of SKAT and other region-based tests is a common unit of aggregation accross studies. This is given in the SNP information file (argument SNPInfo), which pairs SNPs to a unit of aggregation (typically a gene). The additional arguments snpNames and aggregateBy specify the columns of the SNP information file which contain these pairings. Note that the column names of the genotype matrix Z must match the names given in the snpNames field.

Using prepScores, users are strongly recommended to use all SNPs, even if they are monomorphic in your study. This is for two reasons; firstly, monomorphic SNPs provide information about MAF across all studies; without providing the information we are unable to tell if a missing SNP data was monomorphic in a study, or simply failed to genotype adequately in that study. Second, even if some SNPs will be filtered out of a particular meta-analysis (e.g., because they are intronic or common) constructing seqMeta objects describing all SNPs will reduce the workload for subsequent follow-up analyses.

Note: to view results for a single study, one can pass a single seqMeta object to a function for meta-analysis.

an object of class 'seqMeta'. This is a list, not meant for human consumption, but to be fed to skatMeta() or another function. The names of the list correspond to gene names. Each element in the list contains

`scores`	The scores (y-yhat)^t g
`cov`	The variance of the scores. When no covariates are used, this is the LD matrix.
`n`	The number of subjects
`maf`	The alternate allele frequency
`sey`	The residual standard error.

For prepCox, the signed likelihood ratio statistic is used instead of the score, as the score test is anti-conservative for proportional hazards regression. The code for this routine is based on the coxph.fit function from the survival package.

Please see the package vignette for more details.

Arie Voorman, Jennifer Brody

Wu, M.C., Lee, S., Cai, T., Li, Y., Boehnke, M., and Lin, X. (2011) Rare Variant Association Testing for Sequencing Data Using the Sequence Kernel Association Test (SKAT). American Journal of Human Genetics.

Chen H, Meigs JB, Dupuis J. Sequence Kernel Association Test for Quantitative Traits in Family Samples. Genetic Epidemiology. (To appear)

Lin, DY and Zeng, D. On the relative efficiency of using summary statistics versus individual-level data in meta-analysis. Biometrika. 2010.

skatMeta burdenMeta singlesnpMeta skatOMeta coxph

###load example data for two studies:
### see ?seqMetaExample
data(seqMetaExample)

####run on each cohort:
cohort1 <- prepScores(Z=Z1, y~sex+bmi, SNPInfo = SNPInfo, data =pheno1)
cohort2 <- prepScores(Z=Z2, y~sex+bmi, SNPInfo = SNPInfo, kins=kins, data=pheno2)

#### combine results:
##skat
out <- skatMeta(cohort1, cohort2, SNPInfo = SNPInfo)
head(out)

##T1 test
out.t1 <- burdenMeta(cohort1,cohort2, SNPInfo = SNPInfo, mafRange = c(0,0.01))
head(out.t1)

##single snp tests:
out.ss <- singlesnpMeta(cohort1,cohort2, SNPInfo = SNPInfo)
head(out.ss)
## Not run: 
########################
####binary data
cohort1 <- prepScores(Z=Z1, ybin~1, family=binomial(), SNPInfo = SNPInfo, data =pheno1)
out <- skatMeta(cohort1, SNPInfo = SNPInfo)
head(out)

####################
####survival data
cohort1 <- prepCox(Z=Z1, Surv(time,status)~strata(sex)+bmi, SNPInfo = SNPInfo, data =pheno1)
out <- skatMeta(cohort1, SNPInfo = SNPInfo)
head(out)

## End(Not run)

Loading required package: survival
     gene           p     Qmeta      cmaf nmiss nsnps
1   gene1 0.858651041  97982.36 0.2050000   600    15
2  gene10 0.385906585 236430.85 0.3112500  1200    21
3 gene100 0.310106989 201665.04 0.2537500   600    16
4  gene11 0.348914794 298153.58 0.3766667  1800    26
5  gene12 0.005833266 364830.51 0.2175000     0    15
6  gene13 0.716506668 193644.56 0.3095833   600    22
     gene          p        beta        se cmafTotal    cmafUsed nsnpsTotal
1   gene1 0.88817354  0.02859985 0.2033902 0.2050000 0.009583333         15
2  gene10 0.06436376 -0.37607834 0.2033239 0.3112500 0.009583333         21
3 gene100         NA          NA        NA 0.2537500 0.000000000         16
4  gene11         NA          NA        NA 0.3766667 0.000000000         26
5  gene12         NA          NA        NA 0.2175000 0.000000000         15
6  gene13 0.21375342  0.21098043 0.1696925 0.3095833 0.016250000         22
  nsnpsUsed nmiss
1         1     0
2         1     0
3         0     0
4         0     0
5         0     0
6         2   600
   gene    Name         p         maf         caf nmiss ntotal        beta
1 gene1 1000001 0.8283362 0.012916667 0.012916667     0   1200 -0.03808783
2 gene1 1000002 0.4096389 0.014583333 0.014583333     0   1200 -0.13724623
3 gene1 1000003 0.8881735 0.009583333 0.009583333     0   1200  0.02859985
4 gene1 1000004 0.8392653 0.015833333 0.015833333     0   1200 -0.03222936
5 gene1 1000005 0.4976409 0.012083333 0.012083333     0   1200  0.12424703
6 gene1 1000006 0.4737698 0.014166667 0.014166667     0   1200  0.12167820
         se beta.cohort1 se.cohort1 beta.cohort2 se.cohort2
1 0.1756527  -0.06556674  0.2475084  -0.01020464  0.2493225
2 0.1664540  -0.26824737  0.2276350   0.01329406  0.2440214
3 0.2033902  -0.28387603  0.2848673   0.35353121  0.2904894
4 0.1588958  -0.13005243  0.2410367   0.04295373  0.2113112
5 0.1831995  -0.11249089  0.2845093   0.29193007  0.2394457
6 0.1698565   0.11089457  0.2339095   0.13370805  0.2470560
     gene          p    Qmeta      cmaf nmiss nsnps
1   gene1 0.60357255 16429.79 0.2058333     0    15
2  gene10 0.43880917 26284.57 0.3083333   600    20
3 gene100 0.60085665 18150.47 0.2400000     0    16
4  gene11 0.32005395 34476.96 0.3475000  1200    24
5  gene12 0.06862814 30937.60 0.2233333     0    15
6  gene13 0.35458006 29535.92 0.2983333   600    21
Warning messages:
1: In coxlr.fit(cbind(z, X), nullmodel$y, nullmodel$strata, NULL, init = c(0,  :
  Loglik converged before variable  1 ; beta may be infinite. 
2: In coxlr.fit(cbind(z, X), nullmodel$y, nullmodel$strata, NULL, init = c(0,  :
  Loglik converged before variable  1 ; beta may be infinite. 
3: In coxlr.fit(cbind(z, X), nullmodel$y, nullmodel$strata, NULL, init = c(0,  :
  Loglik converged before variable  1 ; beta may be infinite. 
     gene         p     Qmeta      cmaf nmiss nsnps
1   gene1 0.9851753  23122.75 0.2058333     0    15
2  gene10 0.1619854 123747.04 0.3083333   600    20
3 gene100 0.5545533  63668.09 0.2400000     0    16
4  gene11 0.5233723 102691.94 0.3475000  1200    24
5  gene12 0.8078825  45068.24 0.2233333     0    15
6  gene13 0.7262212  66556.76 0.2983333   600    21